EP027 Scientific Computing with SciPy and NumPy

In episode 27, we interviewed Ralf Gommers from the NumPy and SciPy projects. We started our discussion by talking about his past research experience as a physicist and his transition to open source software and programming. This led him to get involved in projects such as PyWavelets, NumPy and SciPy. Following that, we had a great discussion about NumPy, its many features, its target audience and its performance. We learned why NumPy is not included in Python's standard library and its overlap with Scipy. We also compared the combination of Matlab to NumPy and Python and how users could transition to this open source solution. We then had a brief discussion about SciPy and the features it provides. Ralf informed us of the positive results from Google's previous Summer of Code and Season of Docs participations. We discussed how to reach the project and the many kinds of contributions that they are looking for. We talked about the importance of FLOSS for science and attribution of research output. We finished the interview with our classic quick questions and a reflection from Ralf about the need for more sustainability in open source software development as volunteer effort may not be sufficient in the future. 00:00:00 Intro 00:00:18 Introduction 00:00:33 Introducing Ralf Gommers 00:02:05 Research during his PhD and and PostDoc 00:03:20 When he started to use open source tools 00:03:52 Learning to code 00:04:39 PyWavelets, another sideproject he likes 00:05:55 His elevator pitch for NumPy 00:06:55 Vector arrays in Python before NumPy 00:07:49 How he got involved in the NumPy project 00:10:13 Traget users for NumPy 00:11:36 NumPy as part of the standard library? 00:13:24 Features provided by NumPy 00:14:22 Major differences between Python built-in list and NumPy's array 00:16:01 Structured data 00:16:45 Why appending a row to an array is made hard 00:18:09 Multithreaded code with NumPy 00:19:48 Distributed array processing 00:20:50 GPU computation with Python and NumPy 00:22:16 Linear algebra functions in NumPy 00:23:25 Overlap between SciPy and NumPy for linear algebra 00:23:55 Python speed as an interpreted language 00:25:43 Python with NumPy compared to Matlab 00:28:07 How easy is the transition between Matlab and Python Numpy 00:29:26 Performance difference between Matlab and Python 00:31:00 Commercial applications of NumPy 00:32:15 Contributions from the industry ans incentives to contribute 00:34:10 Elevator pitch for SciPy 00:35:37 Overview of some of the submodules in SciPy 00:38:11 The size of the communities 00:39:33 Participation in Google Summer of Code 00:40:24 Participation in Google Season of Docs 00:41:48 Communication channels in the project 00:43:25 Where to ask for support? 00:44:48 Possible contributions 00:46:25 Skills usefull to contribute to the NumPy project 00:48:12 Identifying possible contributions 00:48:52 The importance of FLOSS for science 00:52:02 Possible negative impact of FLOSS on science 00:52:49 Crediting contributions in science 00:53:42 Most notable scientific discovery in recent years 00:54:49 His favourite text processing tool 00:55:30 Volunteer effort may not be sufficient anymore 00:56:58 Contact informations for Ralf Gommers 00:57:27 Outro